Parking management systems are being proposed that provide real-time parking occupancy data to drivers to reduce fuel consumption and traffic congestion. In the context of parking stall occupancy determination, there are various levels of performance metric that must be met depending on the desired applications. For example, one level of performance metric is measured by the accuracy of the total number of spaces available in a parking lot over time. This total number of spaces available can be considered as the lowest (i.e., most achievable) level of information for this application, but also provides the most common and useful information.
Another level of information involves the total number of spaces available for each floor in a parking building (indoors) or the total number of spaces available for each isle/isle-pair (outdoors). This can be useful for providing efficient navigation to a parker entering a large parking lot. The highest level of information can be the state of each parking stall (where are all those available spaces) in the parking lot. If accurate information can be achieved in this level, it opens up several additional applications such as mining parking patterns for better management and configuration, managing/monitoring unexpected parking capacity reduction due to poorly parked vehicles or poor weather condition (snow piled up), etc. Furthermore, high-level information can be easily aggregated to yield the lower level information by a simple summation.
Given such reasons, one would argue why not only develop methods that perform well in providing the highest level information. There are many reasons. For example, many applications require only the lowest level of information. Also, the problem becomes more complex when providing the highest-level of information. There are still many unsolved issues from the perspective of accuracy. Additionally, the computation for methods that aims to determine the individual occupancy of each stall in a lot is much more expensive. There is still a big gap in effectively making them operate in real-time. Other reasons include the fact that the image/video acquisition for methods that aim to determine the individual occupancy of each stall in a parking lot typically requires higher spatial resolutions to perform well (e.g., more expensive camera, higher data rate to deal with). Still, another reason involves the fact that a module that can accurately determine the lower-level information (e.g., total occupancy of a lot) can be helpful in improving a method aiming to provide higher-level information.
Given these reasons, it would be helpful to develop methods for providing highest/higher level of information for parking applications. It is also beneficial to develop methods to provide total occupancy of a parking lot to address the immediate needs of customers.